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1 September 2017 Seed Production Estimation for Mountain Big Sagebrush (Artemisia tridentata ssp. vaseyana)
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Abstract

Seed production is an essential component of postdisturbance recovery for mountain big sagebrush (Artemisia tridentata Nutt, ssp vaseyana [Rydb] Beetle; MBS). We tested a method for rapid estimation of MBS seed production using measurements of inflorescence morphology. We measured total stem length, stem length from first branchlet to stem tip, stem diameter, fresh weight, and number of stem branchlets for 750 inflorescences collected from five central and southern Utah sites. Florets per inflorescence were counted to provide an estimate of seed production potential. We used regression analysis to assess associations between morphological traits and potential seed production and evaluated the efficiency and scalability of each measure for field application. Site means for morphological measures varied ∼2 to 11-fold while mean number of florets per inflorescence varied ∼ 8-fold. Inflorescence weight was the best predictor of seed production potential (P < 0.0001, r2 = 0.897), although correlations for all tested variables were highly significant. Among-site differences in regression equations for this relationship were not significant (P = 0.226), suggesting that a single conversion factor may have broad application. However, validation will require additional testing across a broader range of sites and field conditions. Scalable methods for efficient estimation of sagebrush seed production potential, such as those evaluated in this study, could be useful for managers charged with assessing variability in sagebrush community stability.

© 2017 The Society for Range Management. Published by Elsevier Inc. All rights reserved.
Melissa L. Landeen, Loreen Allphin, Stanley G. Kitchen, and Steven L. Petersen "Seed Production Estimation for Mountain Big Sagebrush (Artemisia tridentata ssp. vaseyana)," Rangeland Ecology and Management 70(5), 633-637, (1 September 2017). https://doi.org/10.1016/j.rama.2017.05.002
Received: 22 December 2016; Accepted: 1 April 2017; Published: 1 September 2017
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